---
title: "Covid-19 Dashboard"
output:
flexdashboard::flex_dashboard:
orientation: row
vertical_layout: fill
theme: simplex
social: ["menu"]
source_code: embed
#Runtime: shiny
#runtime: shiny
---
```{r global, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(plotly)
library(DT)
library(fontawesome)
library(shiny)
library(knitr) # for kable
library(kableExtra)
data = read_csv("D:/Covid-19 Dashboard/ICMR CSV converted data.csv")
#Deleting observations having India as state
data = data[-which(data$district_residence == "INDIA"),]
#deleting other genders only M and F
data = data[-which(is.na(data$gender) == T),]
data = data[-which(data$gender == "T" | data$gender == "O"),]
# Age only in years
data$age_in[data$age_in == "year" | data$age_in == "Year"] = "Years"
data$age_in[data$age_in == "month"] = "Months"
data$age[data$age_in == "Months"] = data$age[data$age_in == "Months"]/12
data$age[data$age_in == "Days"] = data$age[data$age_in == "Days"]/365
# Age only less than 100 years
data = data[-which(data$age > 100),]
#Unique State District
u_s_d = data %>% distinct(state_residence,district_residence)
# Final Test result
data = data %>% mutate(final_test_result = coalesce(final_test_result,orf1b_confirmatory))
data = data %>% mutate(final_test_result = coalesce(final_test_result,rdrp_confirmatory))
data = data %>% mutate(final_test_result = coalesce(final_test_result,egene_screening))
data = data[-which(is.na(data$final_test_result)==T),]
values_to_replace = c("Sample Rejected","ANTIGEN Negative","ANTIGEN Positive","Inconclusive")
replacement_values = c("Sample Rejected", "Antigen Negative", "Antigen Positive", "Inconclusive/Invalid Result")
data <- data %>% mutate(final_test_result = ifelse(final_test_result %in% values_to_replace,
replacement_values[match(final_test_result, values_to_replace)],
final_test_result))
data <- data %>% mutate(final_status = case_when(
final_test_result == "Negative" ~ "Negative",
final_test_result == "TrueNAT Screening Negative" ~ "Negative",
final_test_result == "Antigen Negative" ~ "Negative",
final_test_result == "Positive" ~ "Positive",
final_test_result == "Antigen Positive" ~ "Positive",
final_test_result == "TrueNAT Screening Positive" ~ "Positive",
final_test_result == "Spillage" ~ "Others",
final_test_result == "Under Process" ~ "Others",
final_test_result == "Inconclusive/Invalid Result" ~ "Others",
final_test_result =="Sample Rejected" ~ "Others"))
data = data[-which(data$final_status == "Others"),]
# Creating table
t_pos = data[data$final_status == "Positive",]
t_neg = data[data$final_status == "Negative",]
t1 = data %>% group_by(gender,state_residence,final_test_result,final_status) %>% summarise(Freq = n())
```
# Overview
# Input {.sidebar data-width="250"}
#Covid-19 Dashboard
```{r}
selectizeInput("state",
label = h3("State"),
choices = sort(u_s_d$state_residence),
multiple = F,
selected = NULL,
options = list(
placeholder = 'Select a State',
search = TRUE))
selectizeInput("district", label = h3("District"), choices = NULL, selected = NULL)
renderText({ c_state = input$state
d_state = sort(u_s_d$district_residence[u_s_d$state_residence == c_state])
updateSelectizeInput(session, "district",
choices = d_state ,
selected = NULL)
})
selectizeInput("gender", label = h3("Gender"),
choices = c("M","F"),)
actionButton("run_qa1", " Run Gender Wise")
selectizeInput("final_test",
label = h3("Final Test Result"),
choices = sort(unique(data$final_test_result)),
multiple = F,
selected = NULL)
actionButton("run_qa2", "Run Final Test Result")
selectizeInput("status",
label = h3("Final Status"),
choices = c("Positive", "Negative"),
multiple = F,
selected = NULL)
actionButton("run_qa3", "Run Final Status")
```
Column
------------------------
# All India
Row
---------------------------
```{r}
ggplotly(ggplot(data, aes(x =final_status , y = age, fill = gender)) +
geom_boxplot() +
labs(title = "Box Plots of Age Distribution",
x = "Final Status",
y = "Age (Years)",
fill = "Gender") +
theme_minimal())
```
```{r}
ggplotly(ggplot(t1, aes(x =final_status , y = Freq, fill = gender)) +
geom_boxplot() +
labs(title = "Bar Plots of Gender Distribution",
x = "Final Status",
y = "No. of Patients",
fill = "Gender") +
theme_minimal())
```
```{r}
ggplotly(ggplot(t1, aes(x =final_test_result , y = Freq, fill = gender)) +
geom_boxplot() +
labs(title = "Bar Plots of Gender Distribution",
x = "Final Test Result",
y = "No. of Patients",
fill = "Gender") +
theme_minimal())
ggplotly(ggplot(t1, aes(x =final_status , y = Freq, fill = state_residence)) +
geom_boxplot() +
labs(title = "Bar Plots of State Distribution",
x = "Final Status",
y = "No. of Patients",
fill = "State") +
theme_minimal())
#print("Pie chart showing Gender wise Distribution of Positive Patients in India(%)")
#ggplotly(pie(prop.table(table(t_pos$gender))*100))
#print("Pie chart showing Gender wise Distribution of Negative Patients in India(%)")
#ggplotly(pie(prop.table(table(t_neg$gender))*100))
```
# Visualizations {data-icon="fa-signal"}
```{r}
g <- ggplot(data, aes(gender))
g + geom_bar()
ggplotly(g)
#pie(table(gender))
#pie(table(region))
#pie(table(egene_screening))
# Shiny input for a slider
#sliderInput("bins", "Number of bins:", min = 1, max = 50, value = 30)
# Use the slider input in your visualization
#renderPlot({
# hist(rnorm(input$bins), col = 'lightblue', border = 'black')
#})
```
## Column {data-width="350"}
```{r}
```
```{r}
```
# Tables {data-icon="fa-table"}
Row
---------------------------
### Age Distribution of Positive Patients in India
```{r}
print("Age Distribution of Positive Patients in India")
tww = summary(t_pos$age)
```
### Age Distribution of Negative Patients in India
```{r}
print("Age Distribution of Negative Patients in India")
summary(t_neg$age)
print("Gender wise Distribution of Positive & Negative Patients in India(%)")
prop.table(table(data$gender,data$final_status),margin = 2)*100
print("State wise Distribution of Positive & Negative Patients(%)")
prop.table(table(data$state_residence,data$final_status),margin = 2)*100
print("State wise Distribution of patients based on Final Test Patients(%)")
prop.table(table(data$state_residence,data$final_test_result),margin = 2)*100
```
# Data
```{r}
#data %>% datatable(extensions = "Buttons",
#options = list(dom="Blfrtip",
# button=c("copy", "csv", "excel", "pdf",
# "print")))
library(vtable)
# return will return the data as data.frame but look at other options as well
kable(sumtable(data[,3], out = "return",add.median = T))
```
